Methods and apparatus for providing video with embedded media

ABSTRACT

Methods and apparatus for providing video with embedded media are disclosed. An example method includes dividing an image comprising a number of pixels into a number of portions, each portion including less than all of the pixels of the image, and blending respective ones of the portions with different video frames of a stream of video frames comprising a host presentation, wherein the media reaches a discernibility threshold when the host presentation is played at an accelerated rate.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 12/357,302, filed Jan. 21, 2009, the entirety of which is herebyincorporated by reference. This patent is related to U.S. patentapplication Ser. No. 12/357,315 and U.S. patent application Ser. No.12/357,322.

TECHNICAL FIELD

The present disclosure relates to providing video with embedded media.

BACKGROUND

A variety of conventional systems are available for delivering andmanipulating video. In some instances, personal video recorders ordigital video recorders store video and audio to allow user playbackand/or manipulation of the video. A user may fast forward, rewind, skipforward, and/or play video back at varying speeds. In other instances,video discs may hold video for playback and/or manipulation on videodisc players. Video disc players may similarly allow a user to fastforward, rewind, skip forward, and/or play video back at varying speeds.Computing systems may also hold video in memory that allows playback andmanipulation of the video.

Although a variety of video delivery and manipulation mechanisms areavailable, the ability to embed media in video is limited. Consequently,it is desirable to provide improved methods and apparatus for embeddingmedia in video for user playback and manipulation.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular example embodiments.

FIG. 1 illustrates one example of a system for providing video embeddedmedia.

FIGS. 2A-K illustrate examples of different portions of an embeddedimage and video.

FIG. 3 illustrates one example of a series of video frames.

FIG. 4 illustrates another example of a series of video frames.

FIG. 5 illustrates one example of a system for analyzing video embeddedmedia.

FIG. 6 illustrates one example of a technique for embedding media invideo.

FIG. 7 illustrates one example of technique for performing data analysisfor video embedded media.

FIG. 8 provides one example of a system that can be used to implementone or more mechanisms.

DESCRIPTION OF PARTICULAR EMBODIMENTS

Reference will now be made in detail to some specific examples includingthe best modes contemplated by the inventors for carrying out theinvention. Specific examples are illustrated in the accompanyingdrawings. While specific examples are described below, it will beunderstood that it is not intended to limit the claimed invention to thedescribed examples. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the claimed invention as defined by the claimsof this patent.

For example, techniques and mechanisms are described in the context ofembedding media such as images into video. However, it should be notedthat the techniques and mechanisms of the claimed invention apply to avariety of different types of media such as video and audio. In thefollowing description, numerous specific details are set forth in orderto provide a thorough understanding of the disclosure. Particularexamples may be implemented without some or all of these specificdetails. In other instances, well known process operations have not beendescribed in detail in order not to unnecessarily obscure thedescription.

Various techniques and mechanisms are sometimes described in singularform for clarity. However, it should be noted that some examples includemultiple iterations of a technique or multiple instantiations of amechanism unless noted otherwise. For example, a system uses a processorin a variety of contexts. However, it will be appreciated that a systemcan use multiple processors while remaining within the scope of theclaimed invention unless otherwise noted. Furthermore, the exampletechniques and mechanisms described below sometimes describe aconnection between two entities. It should be noted that a connectionbetween two entities does not necessarily mean a direct, unimpededconnection, as a variety of other entities may reside between the twoentities. For example, a processor may be connected to memory, but itwill be appreciated that a variety of bridges and controllers may residebetween the processor and memory. Consequently, a connection does notnecessarily mean a direct, unimpeded connection unless otherwise noted.

OVERVIEW

An example system modifies video by embedding portions of media, such assubsets of image pixels, in video frames. When the video is played atnormal speed, the media is not discernible. However, when the video isplayed at an accelerated rate in the forward or reverse direction, theportions of images embedded in video frames coalesce into discerniblemedia. The embedded media may be simple text, images, video, audio, orother media. The system may also evaluate base videos as well asinsertion media using neuro-response measurements to determine how andwhat type of media to embed. The media may be embedded in real-time ornear real-time into video for delivery to a user for playback on devicessuch as digital video recorders, computer systems, software and hardwareplayers, cable boxes, etc.

EXAMPLES

Conventional mechanisms for embedding media in video are limited ornon-existent. In some systems, a frame of video may be replaced in itsentirety with a different substitute frame. The single frame may beprocessed subconsciously by a viewer. However, replaced frames aregenerally not looked upon positively. Furthermore, they may have limitedeffectiveness and may not be noticed at all, particularly if the videois being viewed in a time accelerated manner. In other systems, videoincludes watermarking or faint media. However, this media may only bediscernible upon close examination.

Viewers will often fast forward or rewind video data or playback videodata at accelerated rates. Viewers will also often use these mechanismsto skip commercials or portions of content that they do not want to see.As commercial skipping becomes more prevalent, the example techniquesrecognize that it is useful to provide advertisers, content providers,and service providers with mechanisms for introducing additionaldiscernible content to viewers. In some examples, media can beintroduced without any hardware or player modifications. This allowsimage embedding with minimal investment, as no new equipment isrequired.

In some examples, video frames are modified to include differentportions of an image. In some such examples, the different portions maybe different subsets of image pixels or different components of an audioimage. The different image portions may be blended with surroundingimagery to somewhat match hue, saturation, value and/or other imagequalities. When the video is viewed at normal or near normal speeds, theportions of the image and the media itself are not easily discernible.However, when the video is played back at an accelerated speed in eitherthe forward or reverse direction, the different portions of an imagecoalesce to form a discernible image or video. In one example, differentsegments of a line are embedded onto consecutive frames of video. Whenthe frames are played back at 4×, 8×, or 60×speed, the segments combineto create a discernible line. In another example, a subset of the pixelsin a company logo is embedded in the frames of an advertisement. In someexamples, when the video is played at normal speeds, the logo is notdiscernible. However, when the video is played at accelerated speeds,the different subsets of pixels in the different frames combine to forma discernible company logo. In some examples, the company logo is shownwhen a user fast forwards through a commercial.

In still other examples, a video stream is embedded in video content sothat the video stream may be viewed when playback is accelerated. Thevideo stream may be used to enhance a viewing experience, providealternative messages, commercial messages, additional information, orunrelated information altogether. In some examples, the embedded mediamay be text providing location information or a summary about a portionof show being fast forwarded. Instead of watching a 10 minute scene, aviewer may read a summary of the scene or see a title of the scene whilefast forwarding through the 10 minute scene at 8×speed. The summary ortitle would not be discernible when the scene is played at normal speed.In some examples, a discernibility threshold is determined to evaluatewhether media is discernible. In some such examples, survey data is usedto determine a discernibility threshold. In other examples,neuro-response data is used to determine the discernibility threshold.In some examples, a discernibility threshold is reached when 90% of testsubjects notice the embedded media. In other examples, a discernibilitythreshold is reached when 95% of test subjects have neuro-response dataindicating a salient feature at the time embedded media is shown duringaccelerated playback.

In some examples, it may be difficult to effectively introduce mediainto video so that the media is discernible primarily only when playback occurs at an accelerated rate. Consequently, the example techniquesand mechanisms also optionally provide a neuro-response analyzer todetermine the effectiveness of embedded media. The system may alsodetermine what type of media to embed and how to embed the media. Thesystem may also analyze the effectiveness of the resulting video. Thevideo with embedded media may be played on a variety of devices such asdigital video recorders, software players, cable boxes, hardwareplayers, etc. Although media may be embedded, in some examples, mediamay be hidden in a video stream and played when a decoder receives afast forward action.

FIG. 1 illustrates one example of a system for embedding images in avideo. Although one particular example of embedding images isillustrated, it should be noted that a variety of media types such asaudio, changing images, logos, and video can be embedded. Althoughinsertion media is described as being embedded in a base video, in otherexamples, a base video can also be embedded onto the insertion media. Insome examples, the base video may be streaming, file-based, analog,digital, real-time, time-delayed, etc. In some such examples, a videolibrary 111 provides video to a video decoder 113. In some instances,video may not require decoding. In other examples, video may need to bedecompressed and expressed as sequential frames. The system may includea database 121 for images. In some examples, the database may be a mediadatabase that provides media including text, data, logos, pictures,images, and video to an image portion generator 123. The image portiongenerator 123 selects portions of the imagery for inclusion in videoframes. In some examples, the image portion generator 123 randomlyselects subsets of pixels of the image for inclusion in sequentialframes. In some such examples, the image portion generator 123intelligently selects subsets of pixels of the image for inclusion insequential frames.

In some examples, the image portion generator 123 may be connected to avideo decoder 113 to obtain information about the video itself. Theimage portions and video frames are passed to a combined image and videoblender 131. The combined image and video blender 131 melds the imageportions onto the video In some examples, boundaries and colors betweenthe image portions and video are blended. The combined image and videoblender may also identify particular locations in frames for embeddingthe image. In some examples, images are embedded in portions of videothat are relatively static and uniform, such as a part of a video frameshowing a blue sky or a blank wall. Image portions may be made moretransparent, blurred, or generated with lower contrast colors beforeembedding them on the video to make the image portions less visibleduring regular playback. In other examples, images may be outlined moreclearly, made more opaque, or generated with higher contrast colorsbefore embedding them on video to make the images more discernibleduring accelerated playback. In some examples, survey based and/orneuro-response analysis is used to determine the optimal combination orclarity, opacity, and contrast. In other examples, neuro-responseanalysis is used to determine the optimal combination of hue,saturation, and value for various pixels in the image and imageportions.

Video frames embedded with image portions are then passed to videoencoder 133. In some examples, no video encoding is required. The videowith embedded imagery is then stored in a video with embedded imagerylibrary 135. In some such examples, the video is transmitted inreal-time to consumers without any storage mechanism.

FIG. 2A illustrates one example of an image that can be embedded ontovideo. In some examples, the image is a letter “A” in pixel form. FIG.2B shows one frame of a video of a ball rolling down a ramp against anight time sky. FIGS. 2C-2F illustrate portions of an image of theletter “A”. In some examples, a subset of pixels of the image areselected for embedding on each frame. When the frames are viewed atnormal speed, no image is discernible. However, when the frames areplayed at accelerated speeds, the pixels coalesce to form an image.FIGS. 2G-2J show video frames with embedded image portions. FIGS. 2G-2Jinclude embedded images in FIGS. 2C-2F respectively. FIG. 2K shows afull image of the letter “A” embedded on a frame in 2K. In someexamples, the full image of the letter “A” is what is discernible whenthe frames are played at an accelerated rate.

FIG. 3 illustrates one example of a sequence of frames. Video includesframes 311, 313, 315, 317, 319, 321, 323, 325, 327, 329, 331, and 333.Image portions 301, 303, 305, and 307 are provided for inclusion invideo frames. In some examples, image portion 301 is included in frame313, image portion 303 is included in frame 315, image portion 305 isincluded in frame 317, and image portion 307 is included in frame 319.In some such examples, image portions are included in sequential frames.However, in many instances, not every frame needs to have embedded imageportions. In some examples, multiple frames in a sequence include thesame image portion.

FIG. 4 illustrates another example of a sequence of frames. Many videoencoding mechanisms include different types of frames. In some examples,frames include intra-coded frames (I-frames), predicted frames(P-frames), and bi-predictive frames (B-frames). I-frames providesubstantially all of the data needed to present a full picture. On theother hand, P-frames and B-frames provide information about differencesbetween the predictive frame and an I-frame. Predictive frames such asB-frames and P-frames are smaller and more bandwidth efficient thanI-frames. In some examples, the techniques modify only I-frames. In somesuch examples, only I-frames are embedded with media portions.

In some examples, frames sequences 411, 413, 415, 417, 419, 421, 423,425, 427, 429, 431, and 433 include I-frames 411, 419, 425, and 433. Theframe sequence also includes predictive frames including P-frames 413,417, 421, 423, and 427 as well as B-frames 415, 429, and 431. In somesuch examples, image portions are embedded on I-frames. Pixel subsetsare shown as examples of portions of an image A. Image portion 401 isblended with I-frame 411, image portion 403 is blended with I-frame 419,image portion 405 is blended with I-frame 425, and image portion 407 isblended with I-frame 433.

A variety of survey based and neuro-response based mechanisms can beused to determine the effectiveness of embedding media into video. Usingfeedback from survey based and/or neuro-response based mechanisms canallow adjustment of the type of media, the saliency of the media in thevideo, the location of the media, duration and size of the media, anddynamism of the media. For example, survey based and/or neuro-responsemechanisms may determine that media is hardly noticed by a majority ofviewers even when the video is played at a variety of acceleratedspeeds. In some such examples, the media contrast and size may beincreased. In other examples, survey based and/or neuro-responsemechanisms may indicate that media is noticeable and distracting evenduring playback at regular speeds. Additional blending mechanisms may beapplied to an image to reduce the noticeability of an image duringregular playback. The position or size of the image may also beadjusted.

FIG. 5 illustrates one example of a system for evaluating imageryembedded in video using central nervous system, autonomic nervoussystem, and/or effector measures. In some examples, the video embeddedimagery system includes a stimulus presentation device 501. In some suchexamples, the stimulus presentation device 501 is merely a display,monitor, screen, etc., that displays stimulus material to a user. Thestimulus material may be videos with embedded media or the media itself.Continuous and discrete modes are supported. In some examples, thestimulus presentation device 501 also has protocol generation capabilityto allow intelligent customization of stimuli provided to multiplesubjects in different markets.

In some examples, stimulus presentation device 501 could include devicessuch as televisions, cable consoles, computers and monitors, projectionsystems, display devices, speakers, tactile surfaces, etc., forpresenting the video from different networks, local networks, cablechannels, syndicated sources, websites, internet content aggregators,portals, service providers, etc.

In some examples, the subjects 503 are connected to data collectiondevices 505. The data collection devices 505 may include a variety ofneuro-response measurement mechanisms including neurological andneurophysiological measurements systems. In some examples,neuro-response data includes central nervous system, autonomic nervoussystem, and effector data.

Some examples of central nervous system measurement mechanisms includeFunctional Magnetic Resonance Imaging (fMRI) and Electroencephalography(EEG). fMRI measures blood oxygenation in the brain that correlates withincreased neural activity. However, current implementations of fMRI havepoor temporal resolution of few seconds. EEG measures electricalactivity associated with post synaptic currents occurring in themilliseconds range. Subcranial EEG can measure electrical activity withthe most accuracy, as the bone and dermal layers weaken transmission ofa wide range of frequencies. Nonetheless, surface EEG provides a wealthof electrophysiological information if analyzed properly.

Autonomic nervous system measurement mechanisms include Galvanic SkinResponse (GSR), Electrocardiograms (EKG), pupillary dilation, etc.Effector measurement mechanisms include Electrooculography (EOG), eyetracking, facial emotion encoding, reaction time etc.

In some examples, the techniques and mechanisms intelligently blendmultiple modes and manifestations of precognitive neural signatures withcognitive neural signatures and post cognitive neurophysiologicalmanifestations to more accurately allow assessment of embedded imageryin video. In some examples, autonomic nervous system measures arethemselves used to validate central nervous system measures. Effectorand behavior responses are blended and combined with other measures. Insome examples, central nervous system, autonomic nervous system, andeffector system measurements are aggregated into a measurement thatallows definitive evaluation stimulus material.

In some examples, the data collection devices 505 include EEG 511, EOG513, and GSR 515. In some instances, only a single data collectiondevice is used. Data collection may proceed with or without humansupervision.

The data collection device 505 collects neuro-response data frommultiple sources. This includes a combination of devices such as centralnervous system sources (EEG), autonomic nervous system sources (GSR,EKG, pupillary dilation), and effector sources (EOG, eye tracking,facial emotion encoding, reaction time). In some examples, datacollected is digitally sampled and stored for later analysis. In someexamples, the data collected could be analyzed in real-time. In someexamples, the digital sampling rates are adaptively chosen based on theneurophysiological and neurological data being measured.

In some examples, the video embedded imagery system includes EEG 511measurements made using scalp level electrodes, EOG 513 measurementsmade using shielded electrodes to track eye data, GSR 515 measurementsperformed using a differential measurement system, a facial muscularmeasurement through shielded electrodes placed at specific locations onthe face, and a facial affect graphic and video analyzer adaptivelyderived for each individual.

In some examples, the data collection devices are clock synchronizedwith a stimulus presentation device 501. In some such examples, the datacollection devices 505 also include a condition evaluation subsystemthat provides auto triggers, alerts and status monitoring andvisualization components that continuously monitor the status of thesubject, data being collected, and the data collection instruments. Thecondition evaluation subsystem may also present visual alerts andautomatically trigger remedial actions. In some examples, the datacollection devices include mechanisms for not only monitoring subjectneuro-response to stimulus materials, but also include mechanisms foridentifying and monitoring the stimulus materials. For example, datacollection devices 505 may be synchronized with a set-top box to monitorchannel changes. In other examples, data collection devices 505 may bedirectionally synchronized to monitor when a subject is no longer payingattention to stimulus material. In still other examples, the datacollection devices 505 may receive and store stimulus material generallybeing viewed by the subject, whether the stimulus is a program, acommercial, printed material, or a scene outside a window. The datacollected allows analysis of neuro-response information and correlationof the information to actual stimulus material and not mere subjectdistractions.

In some examples, the video embedded imagery system also includes a datacleanser and analyzer device 521. In some such examples, the datacleanser and analyzer device 521 filters the collected data to removenoise, artifacts, and other irrelevant data using fixed and adaptivefiltering, weighted averaging, advanced component extraction (like PCA,ICA), vector and component separation methods, etc. This device cleansesthe data by removing both exogenous noise (where the source is outsidethe physiology of the subject, e.g. a phone ringing while a subject isviewing a video) and endogenous artifacts (where the source could beneurophysiological, e.g. muscle movements, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

In some examples, the data cleanser and analyzer device 521 isimplemented using hardware, firmware, and/or software.

The data analyzer portion uses a variety of mechanisms to analyzeunderlying data in the system to determine resonance. In some examples,the data analyzer customizes and extracts the independent neurologicaland neuro-physiological parameters for each individual in each modality,and blends the estimates within a modality as well as across modalitiesto elicit an enhanced response to the presented stimulus material. Insome such examples, the data analyzer aggregates the response measuresacross subjects in a dataset.

In some examples, neurological and neuro-physiological signatures aremeasured using time domain analyses and frequency domain analyses. Suchanalyses use parameters that are common across individuals as well asparameters that are unique to each individual. The analyses could alsoinclude statistical parameter extraction and fuzzy logic based attributeestimation from both the time and frequency components of thesynthesized response.

In some examples, statistical parameters used in a blended effectivenessestimate include evaluations of skew, peaks, first and second moments,distribution, as well as fuzzy estimates of attention, emotionalengagement and memory retention responses.

In some examples, the data analyzer may include an intra-modalityresponse synthesizer and a cross-modality response synthesizer. In somesuch examples, the intra-modality response synthesizer is configured tocustomize and extract the independent neurological andneurophysiological parameters for each individual in each modality andblend the estimates within a modality analytically to elicit an enhancedresponse to the presented stimuli. In some examples, the intra-modalityresponse synthesizer also aggregates data from different subjects in adataset.

In some examples, the cross-modality response synthesizer or fusiondevice blends different intra-modality responses, including raw signalsand signals output. The combination of signals enhances the measures ofeffectiveness within a modality. The cross-modality response fusiondevice can also aggregate data from different subjects in a dataset.

In some examples, the data analyzer also includes a composite enhancedeffectiveness estimator (CEEE) that combines the enhanced responses andestimates from each modality to provide a blended estimate of theeffectiveness. In some such examples, blended estimates are provided foreach exposure of a subject to stimulus materials. The blended estimatesare evaluated over time to assess resonance characteristics. In someexamples, numerical values are assigned to each blended estimate. Thenumerical values may correspond to the intensity of neuro-responsemeasurements, the significance of peaks, the change between peaks, etc.Higher numerical values may correspond to higher significance inneuro-response intensity. Lower numerical values may correspond to lowersignificance or even insignificant neuro-response activity. In otherexamples, multiple values are assigned to each blended estimate. Instill other examples, blended estimates of neuro-response significanceare graphically represented to show changes after repeated exposure.

In some examples, a data analyzer passes data to a resonance estimatorthat assesses and extracts resonance patterns. In some such examples,the resonance estimator determines entity positions in various stimulussegments and matches position information with eye tracking paths whilecorrelating saccades with neural assessments of attention, memoryretention, and emotional engagement. In some examples, the resonanceestimator stores data in the priming repository system. As with avariety of the components in the system, various repositories can beco-located with the rest of the system and the user, or could beimplemented in remote locations.

FIG. 6 illustrates an example of a technique for providing video withembedded media such as imagery. In some examples, static or changingimagery is embedded in video so that the image is only discernible whenthe video is being viewed at an accelerated rate such as during fastforward or rewind. At 601, an image is received. The selected image maybe text, graphics, or other data. In some instances, multiple images canbe selected for a single video. The multiple images may also be selectedframes of another video. At 603, the image is divided into portions. Insome examples, a subset of image pixels are selected for each imageportion, so that a sequence of image portions would includesubstantially all of the image pixels. In some such examples, the numberof subsets of image pixels generated is equal to the standardaccelerated fast forward or rewind rate. For example, if the standardfast forward rate is 4×, four subsets of image pixels are generatedwhere each subset includes approximately one quarter of the total imagepixels. In another example, eight subsets of image pixels are generatedwhere each subset include approximately one sixth of the total imagepixels. Survey based and neuro-response based feedback can be used toselect the number of subsets generated and the percentage of total imagepixels to include in each subset.

At 605, video is received and decoded. The video may include intra-codedframes as well as predictive frames. In other examples, the video isanalog video and may not require decoding. At 609, video frames areblended with image portions. In some examples, only intra-coded framesare blended with image portions and predictive frames remain unchanged.Hue, saturation, and value, etc. of image portion pixels may be blendedwith surrounding video pixels. Value may be associated with brightnessand intensity or contrast. In some examples, hue relates to differentdominant wavelengths of light, such as red, purple, blue, etc.

The way a viewer perceives color may also vary along other dimensions.One of the dimensions is value, or lightness and darkness. In terms of aspectral definition of color, value describes the overall intensity orstrength of the light. Another dimension is saturation. Saturationrefers to the dominance of hue in the color. Desaturated colorsconstitute different scales of gray, running from white to black.Individual pixels in the plurality of image portions may be adjusted forhue, saturation, and value in order to blend the image effectively. At611, video may be encoded.

The size, type, location of images as well as the amount of blending touse can be determined for particular images and video using survey basedand neuro-response based feedback.

FIG. 7 illustrates one example of using neuro-response based feedbackfor providing video embedded media. At 701, stimulus material isprovided to multiple subjects. In some examples, stimulus includesstreaming video and audio. In some such examples, subjects view stimulusin their own homes in group or individual settings. In some examples,verbal and written responses are collected for use withoutneuro-response measurements. In other examples, verbal and writtenresponses are correlated with neuro-response measurements. At 703,subject neuro-response measurements are collected using a variety ofmodalities, such as EEG, ERP, EOG, etc. At 705, data is passed through adata cleanser to remove noise and artifacts that may make data moredifficult to interpret. In some examples, the data cleanser removes EEGelectrical activity associated with blinking and otherendogenous/exogenous artifacts.

In some examples, data analysis is performed. Data analysis may includeintra-modality response synthesis and cross-modality response synthesisto enhance effectiveness measures. It should be noted that in someparticular instances, one type of synthesis may be performed withoutperforming other types of synthesis. For example, cross-modalityresponse synthesis may be performed with or without intra-modalitysynthesis.

A variety of mechanisms can be used to perform data analysis. In somesuch examples, a stimulus attributes repository is accessed to obtainattributes and characteristics of the stimulus materials, along withpurposes, intents, objectives, etc. In some examples, EEG response datais synthesized to provide an enhanced assessment of effectiveness. Insome examples, EEG measures electrical activity resulting from thousandsof simultaneous neural processes associated with different portions ofthe brain. EEG data can be classified in various bands. In someexamples, brainwave frequencies include delta, theta, alpha, beta, andgamma frequency ranges. Delta waves are classified as those less than 4Hz and are prominent during deep sleep. Theta waves have frequenciesbetween 3.5 to 7.5 Hz and are associated with memories, attention,emotions, and sensations. Theta waves are typically prominent duringstates of internal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms recognize that analyzing highgamma band (kappa-band: Above 60 Hz) measurements, in addition to theta,alpha, beta, and low gamma band measurements, enhances neurologicalattention, emotional engagement and retention component estimates. Insome examples, EEG measurements including difficult to detect high gammaor kappa band measurements are obtained, enhanced, and evaluated.Subject and task specific signature sub-bands in the theta, alpha, beta,gamma and kappa bands are identified to provide enhanced responseestimates. In some examples, high gamma waves (kappa-band) above 80 Hz(typically detectable with sub-cranial EEG and/ormagnetoencephalograophy) can be used in inverse model-based enhancementof the frequency responses to the stimuli.

Various examples recognize that particular sub-bands within eachfrequency range have particular prominence during certain activities. Asubset of the frequencies in a particular band is referred to herein asa sub-band. For example, a sub-band may include the 40-45 Hz rangewithin the gamma band. In particular embodiments, multiple sub-bandswithin the different bands are selected while remaining frequencies areband pass filtered. In particular embodiments, multiple sub-bandresponses may be enhanced, while the remaining frequency responses maybe attenuated.

An information theory based band-weighting model is used for adaptiveextraction of selective dataset specific, subject specific, taskspecific bands to enhance the effectiveness measure. Adaptive extractionmay be performed using fuzzy scaling. Stimuli can be presented andenhanced measurements determined multiple times to determine thevariation profiles across multiple presentations. Determining variousprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. The synchronous response of multiple individuals to stimulipresented in concert is measured to determine an enhanced across subjectsynchrony measure of effectiveness. In some examples, the synchronousresponse may be determined for multiple subjects residing in separatelocations or for multiple subjects residing in the same location.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied—in sequence orin parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhancedsignificance data, additional cross-modality synthesis mechanisms canalso be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR,EOG, and facial emotion encoding are connected to a cross-modalitysynthesis mechanism. Other mechanisms as well as variations andenhancements on existing mechanisms may also be included. In someexamples, data from a specific modality can be enhanced using data fromone or more other modalities. In particular embodiments, EEG typicallymakes frequency measurements in different bands like alpha, beta andgamma to provide estimates of significance. However, the techniquesrecognize that significance measures can be enhanced further usinginformation from other modalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of significance including but not limited to attention,emotional engagement, and memory retention. In some examples, across-modality synthesis mechanism performs time and phase shifting ofdata to allow data from different modalities to align. In some examples,it is recognized that an EEG response will often occur hundreds ofmilliseconds before a facial emotion measurement changes. Correlationscan be drawn and time and phase shifts made on an individual as well asa group basis. In other examples, saccadic eye movements may bedetermined as occurring before and after particular EEG responses. Insome examples, time corrected GSR measures are used to scale and enhancethe EEG estimates of significance including attention, emotionalengagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. In some examples, ERP measures are enhanced using EEGtime-frequency measures (ERPSP) in response to the presentation of themarketing and entertainment stimuli. Specific portions are extracted andisolated to identify ERP, DERP and ERPSP analyses to perform. Inparticular embodiments, an EEG frequency estimation of attention,emotion and memory retention (ERPSP) is used as a co-factor in enhancingthe ERP, DERP and time-domain response analysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. In some examples,EOG and eye tracking is enhanced by measuring the presence of lambdawaves (a neurophysiological index of saccade effectiveness) in theongoing EEG in the occipital and extra striate regions, triggered by theslope of saccade-onset to estimate the significance of the EOG and eyetracking measures. In particular embodiments, specific EEG signatures ofactivity such as slow potential shifts and measures of coherence intime-frequency responses at the Frontal Eye Field (FEF) regions thatpreceded saccade-onset are measured to enhance the effectiveness of thesaccadic activity data.

GSR typically measures the change in general arousal in response tostimulus presented. In some examples, GSR is enhanced by correlatingEEG/ERP responses and the GSR measurement to get an enhanced estimate ofsubject engagement. The GSR latency baselines are used in constructing atime-corrected GSR response to the stimulus. The time-corrected GSRresponse is co-factored with the EEG measures to enhance GSRsignificance measures.

In some examples, facial emotion encoding uses templates generated bymeasuring facial muscle positions and movements of individualsexpressing various emotions prior to the testing session. Theseindividual specific facial emotion encoding templates are matched withthe individual responses to identify subject emotional response. Inparticular embodiments, these facial emotion encoding measurements areenhanced by evaluating inter-hemispherical asymmetries in EEG responsesin specific frequency bands and measuring frequency band interactions.The techniques recognize that not only are particular frequency bandssignificant in EEG responses, but particular frequency bands used forcommunication between particular areas of the brain are significant.Consequently, these EEG responses enhance the EMG, graphic and videobased facial emotion identification.

In some examples, post-stimulus versus pre-stimulus differentialmeasurements of ERP time domain components in multiple regions of thebrain (DERP) are measured at 707. The differential measures give amechanism for eliciting responses attributable to the stimulus. Forexample the messaging response attributable to an advertisement or thebrand response attributable to multiple brands is determined usingpre-resonance and post-resonance estimates.

At 709, target versus distracter stimulus differential responses aredetermined for different regions of the brain (DERP). At 711, eventrelated time-frequency analysis of the differential response (DERPSPs)are used to assess the attention, emotion and memory retention measuresacross multiple frequency bands. In some examples, the multiplefrequency bands include theta, alpha, beta, gamma and high gamma orkappa. At 713, priming levels and resonance for various products,services, and offerings are determined at different locations in thestimulus material. In some examples, priming levels and resonance aremanually determined. In other examples, priming levels and resonance areautomatically determined using neuro-response measurements. In someexamples, video streams are modified with different insertedadvertisement images for various products and services to determine theeffectiveness of the inserted advertisement images based on priminglevels and resonance of the source material.

At 717, multiple trials are performed to enhance priming and resonancemeasures. In some examples, stimulus. In some examples, multiple trialsare performed to enhance resonance measures.

In particular examples, the priming and resonance measures are sent to apriming repository 719. The priming repository 719 may be used toautomatically select images for insertion into video.

In some examples, various mechanisms such as the data collectionmechanisms, the intra-modality synthesis mechanisms, cross-modalitysynthesis mechanisms, etc. are implemented on multiple devices. However,it is also possible that the various mechanisms be implemented inhardware, firmware, and/or software in a single system. FIG. 8 providesone example of a system that can be used to implement one or moremechanisms. For example, the system shown in FIG. 8 may be used toimplement an video embedded imagery system.

According to particular examples, a system 800 includes a processor 801,a memory 803, an interface 811, and a bus 815 (e.g., a PCI bus). Whenacting under the control of appropriate software or firmware, theprocessor 801 is responsible for such tasks such as pattern generation.Various specially configured devices can also be used in place of aprocessor 801 or in addition to processor 801. The completeimplementation can also be done in custom hardware. The interface 811 istypically configured to send and receive data packets or data segmentsover a network. Particular examples of interfaces the device supportsinclude host bus adapter (HBA) interfaces, Ethernet interfaces, framerelay interfaces, cable interfaces, DSL interfaces, token ringinterfaces, and the like.

According to particular examples, the system 800 uses memory 803 tostore data, algorithms and program instructions. The programinstructions may control the operation of an operating system and/or oneor more applications, for example. The memory or memories may also beconfigured to store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present inventionrelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks and DVDs;magneto-optical media such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory devices (ROM) and random access memory (RAM).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Although examples have been described in some detail for purposes ofclarity of understanding, it will be apparent that certain changes andmodifications may be practiced within the scope of the claims of thispatent. Therefore, the described examples are to be considered asillustrative and not restrictive and the claimed invention is notlimited to the details given herein. The claims of this patent are to begiven their full scope including equivalents.

1. A method, comprising: dividing an image comprising a number of pixelsinto a number of portions, each portion including less than all of thepixels of the image; and blending respective ones of the portions withdifferent video frames of a stream of video frames comprising a hostpresentation, wherein the image reaches a discernibility threshold whenthe host presentation is played at an accelerated rate.
 2. The method ofclaim 1, wherein the number of portions is equal to the acceleratedrate.
 3. The method of claim 1, wherein the number of portions is equalto a multiple or a fraction of the accelerated rate.
 4. The method ofclaim 1, wherein the accelerated rate is one of a fast forward or arewind.
 5. The method of claim 1, wherein the different video frames areseparated by intermediate video frames.
 6. The method of claim 1,wherein dividing the image comprises evenly dividing the image.
 7. Themethod of claim 1, wherein the portions are blended into a visiblecomponent of the video frame, the visible component being viewable by aviewer during normal playback.
 8. The method of claim 1, wherein theportions, when viewed at the accelerated rate provide a summary of thehost presentation.
 9. The method of claim 1, wherein a subset of thepixels in a first one of the portions are also present in a second oneof the portions.
 10. The method of claim 1, wherein dividing the imagecomprises randomly selecting the pixels of each portion.
 11. The methodof claim 1, wherein dividing the image comprises intelligently selectingthe pixels of each portion.
 12. The method of claim 1, wherein blendingthe portions into the video frames comprises intelligently selecting theportions of the video frames to blend.
 13. The method of claim 1,further comprising intelligently selecting at least one of a color, acontrast, a brightness, a hue, or a saturation of one of the portionsbased on corresponding image attributes of the video frame into whichthe one of the portions is blended.
 14. The method of claim 1, whereinthe portions collectively include substantially all of the pixels of theimage.
 15. The method of claim 1, wherein the number of portions equalsa standard accelerated fast forward rate.
 16. The method of claim 1,wherein the accelerated rate is x and each portion includes 1/x a totalnumber of pixels in the image.
 17. The method of claim 1, wherein theaccelerated rate is x and a number of the portions is x.
 18. The methodof claim 1, wherein the number of portions is eight and each portionincludes approximately one-sixth a total number of pixels in the image.19. The method of claim 1, wherein individual pixels are adjusted for atleast one of hue, saturation or value to blend the pixels in the video.20. The method of claim 1, wherein the image does not reach thediscernibility threshold when the video is played at normal speed.
 21. Amethod, comprising: dividing media into a number of portions, the numberof portions being equal to an expected fast forward rate, each portionincluding less than all pixels of the media; and blending respectiveones of the portions with corresponding video frames of a stream ofvideo frames comprising a host presentation, wherein the media reaches adiscernibility threshold when the host presentation is played at thefast forward rate.
 22. A system, comprising: a processor to dividepixels of media into a number of subsets, each subset including lessthan all the pixels of the media; and an encoder to blend the subsetswith respective ones of video frames of a stream of video framescomprising a host presentation, wherein the media reaches adiscernibility threshold when the host presentation is played at anaccelerated rate.
 23. The system of claim 22, wherein the number ofsubsets is equal to the accelerated rate.
 24. The system of claim 22,wherein the number of subsets is equal to a multiple or a fraction ofthe accelerated rate.
 25. The system of claim 22, wherein theaccelerated rate is one of a fast forward or a rewind.
 26. The system ofclaim 22, wherein the video frames into which respective portions of themedia are blended are separated by intermediate video frames.
 27. Thesystem of claim 22, wherein the processor is to evenly divide the mediainto the subsets.
 28. The system of claim 22, wherein the encoder is toblend the pixels into a visible component of the video frame, thevisible component being viewable by a viewer during normal playback. 29.The system of claim 22, wherein the subsets, when viewed at theaccelerated rate provide a summary of the host presentation.
 30. Thesystem of claim 22, wherein one or more pixels in a first subset arealso present in a second subset.
 31. The system of claim 22, wherein themedia does not reach the discernibility threshold when the video isplayed at normal speed.
 32. A machine readable storage medium comprisinginstructions, which when read, cause a machine to at least: divide animage comprising a number of pixels into a number of portions, eachportion including less than all of the pixels of the image; and blendrespective ones of the portions with different video frames of a streamof video frames comprising a host presentation, wherein the imagereaches a discernibility threshold when the host presentation is playedat an accelerated rate.
 33. The medium of claim 32, wherein the numberof portions is equal to the accelerated rate.
 34. The medium of claim32, wherein the number of portions is equal to a multiple or a fractionof the accelerated rate.
 35. The medium of claim 32, wherein theaccelerated rate is one of a fast forward or a rewind.
 36. The medium ofclaim 32, wherein the different video frames are separated byintermediate video frames.
 37. The medium of claim 32, wherein theinstructions cause the machine to evenly divide the image.
 38. Themedium of claim 32, wherein the instructions cause the machine to blendthe portions into a visible component of the video frame, the visiblecomponent being viewable by a viewer during normal playback.
 39. Themedium of claim 32, wherein the portions, when viewed at the acceleratedrate provide a summary of the host presentation.
 40. The medium of claim32, wherein a subset of the pixels of a first one of the portions arealso present in a second one of the portions.
 41. The medium of claim32, wherein the image does not reach the discernibility threshold whenthe video is played at normal speed.